Several questions about CIFAR-10S Codes

[todayplusplus]

1) shape error in test() of train.py

In line 308 of function test() in train.py

Running it directly will result in an error, after added an exception handler that showed that there was a problem with the targets.data's shape,

the output is

Since shape of target.data is torch.Size([100]), use _, max_likely = torch.max(targets.data, 1) will raise an error.

I fix this bug by directly max_likely = targets.data but the output is worse, all of the loss is NAN

I would like to know if I made any mistakes that resulted in the error. I hope you can check your original code. Thank you.

2) May be some slip of a pen in utils.py

line 239 of train.py we use criterion = utils.cross_entropy_loss

But in utils.py the function is def cross_entropy(preds, trgts, num_class=10), notice that the Function parameters seems to be slip of a pen? i guess that you may want to use def cross_entropy(preds, trgts, num_classes=10), since In train.py you use loss = criterion(outputs, targets, num_classes=num_classes) instead of num_class=num_classes.

And at last, some libraries are also imported in the code, but they do not exist, such as
from data import CIFARMixHILL

[collinskatie]

Hi @todayplusplus , thanks for raising these issues! We appreciate your rigorous write-up. I apologize for the challenges you encountered with our code!

The original codebase was jointly structured with our HILL mixup work (https://arxiv.org/abs/2211.01202), as I worked on them in tandem for my MPhil thesis. I tried to repartition the code, but forgot to remove that line!

For the num_classes, yes this is a typo, apologies! You can see though that we override the num_classes here so it would not have an impact (but is still not good code): https://github.com/cambridge-mlg/cifar-10s/blob/master/computational_experiments/utils.py#L67

As for the other shaping bug -- what data are you using as your train / test data? The target should be an object of size: [batch_size, num_classes]. But it appears to not be doing that here?

Perhaps in test...:

if len(targets.shape) == 1:  #  if scalar index -> one-hot encode
        targets = F.one_hot(targets, num_classes=num_classes)

Again, apologies that this code is in a bit of a messy state; we intended to write an updated training using huggingface / timm (https://huggingface.co/docs/timm/index), as this code is somewhat outdated, but have not yet done so.

[todayplusplus]

Thank you for your patient response. I will review my code based on your suggestions and hints.
Thank you again, best wishes.